This project encompasses a wide scope of statistical collaboration with neuroscientists and clinical neurologists,and the developoment of statistical methods to address problems encountered in collaborative studies. The focus of collaborative research is on statistical planning and design of experiments, statistical analysis and modeling of data, and statistical inference. Examples of current studies include: prevalence studies of neurological diseases in Argentina, Italy and Spain; validation study of consultations provided by U. S. drug information centers; case-control study of ultra microassay of neonatal blood constituents for increased cerebral palsy risk and autism; analysis of the relationship of cytokines. neurotrophins and growth factors cerebral palsy and autism risk; clinical trial of the effectiveness of magnesium sulfate for the prevention of cerebral palsy in infants from high-risk pregnancies; statistical modeling and estimation of variance for the occurrence of Gd-enhancing lesions over time in patients with relapsing-remitting multiple sclerosis; identification of clinical factors known at hospital admission that are associated with outcome of ischemic stroke; design and analysis of clinical trials for the evaluation of stroke rehabilitation strategies; population prevalence studies of epilepsy; analysis of developmental brain differences between childhood onset schizophrenic patients and healthy controls; longitudinal modeling of gray/white matter development in healthy children; longitudinal MRI study of corpus callosum development in healthy children; statistical models for spectroscopic imaging of the brain; analysis and statistical modeling of mechanisms of use-dependent plasticity in human motor cortex; the relationship of seizure frequency to hippocampus volume in temporal epilepsy; and post operative changes in cerebral metabolism in temporal lobe epilepsy. Statistical research usually derives from problems encountered in collaborative studies. Areas of research include: population screening methods for disease; establishment of reference range values for diagnostic tests; time series models for longitudinal imaging data; development of recursive partioning methods for the identification of risk factors in epidemiologic studies; derivation of nonparametric regressions for the analysis of imaging data; quantification of selection bias in surveys; nonlinear response models for nondecreasing, time-dependent multiple compartment systems; and new likelihood tests for logistic mixture models in time series.